https://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-018-0410-y
- Sangjun Lee,
- Jinsoo Kim†,
- Lauren Baker†,
- Andrew Long,
- Nikos Karavas,
- Nicolas Menard,
- Ignacio Galiana and
- Conor J. Walsh
†Contributed equally
Journal of NeuroEngineering and Rehabilitation201815:66
© The Author(s). 2018
Received: 15 December 2017
Accepted: 3 July 2018
Published: 13 July 2018
Abstract
Background
Soft exosuits are a recent approach for assisting human locomotion, which apply assistive torques to the wearer through functional apparel. Over the past few years, there has been growing recognition of the importance of control individualization for such gait assistive devices to maximize benefit to the wearer. In this paper, we present an updated version of autonomous multi-joint soft exosuit, including an online parameter tuning method that customizes control parameters for each individual based on positive ankle augmentation power.
Methods
The soft exosuit is designed to assist with plantarflexion, hip flexion, and hip extension while walking. A mobile actuation system is mounted on a military rucksack, and forces generated by the actuation system are transmitted via Bowden cables to the exosuit. The controller performs an iterative force-based position control of the Bowden cables on a step-by-step basis, delivering multi-articular (plantarflexion and hip flexion) assistance during push-off and hip extension assistance in early stance. To individualize the multi-articular assistance, an online parameter tuning method was developed that customizes two control parameters to maximize the positive augmentation power delivered to the ankle. To investigate the metabolic efficacy of the exosuit with wearer-specific parameters, human subject testing was conducted involving walking on a treadmill at 1.50 m s− 1 carrying a 6.8-kg loaded rucksack. Seven participants underwent the tuning process, and the metabolic cost of loaded walking was measured with and without wearing the exosuit using the individualized c
ontrol parameters.
Results
The online parameter tuning method was capable of customizing the control parameters, creating a positive ankle augmentation power map for each individual. The subject-specific control parameters and resultant assistance profile shapes varied across the study participants. The exosuit with the wearer-specific parameters significantly reduced the metabolic cost of load carriage by 14.88 ± 1.09% (P = 5 × 10− 5) compared to walking without wearing the device and by 22.03 ± 2.23% (P = 2 × 10− 5) compared to walking with the device unpowered.
Conclusion
The autonomous multi-joint soft exosuit with subject-specific control parameters tuned based on positive ankle augmentation power demonstrated the ability to improve human walking economy. Future studies will further investigate the effect of the augmentation-power-based control parameter tuning on wearer biomechanics and energetics.
Keywords
ExosuitAssistanceTuningAugmentation powerMetabolic cost
Introduction
Lower-limb assistive devices have been designed to assist with human locomotion [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]. Recently, different groups have used rigid but lightweight mechanisms to create low-profile exoskeletons assisting with a specific target joint, and studies have shown that these devices may substantially reduce the energy cost of loaded [6] and unloaded [7, 8, 9, 10, 11, 12] wa
lking. For example, Lee et al. showed that their hip exoskeleton reduced the metabolic cost of walking by 21% compared to walking without wearing the device [12]. For ankle, Mooney et al. reported an 11% net benefit for walking [10] and an 8% net benefit for load carriage [6] using their autonomous ankle exoskeleton.
Our group has been developing soft exosuits that use functional textiles to anchor to the body and deliver assistance in parallel with the underlying muscles [13, 14, 15, 16, 17, 18, 19, 20]. In studies with tethered versions of the device, exosuits have been shown to significantly reduce the energy cost of regular walking [17, 20], walking with load [16, 19], and running [18]. For an autonomous version, Panizzolo et al. showed a 7% net metabolic reduction for loaded walking compared to equivalent-mass-removed condition (walking with the device unpowered but removing the equivalent mass of the device) [15].
Over the past few years, there has been a growing recognition on the importance of control individualization for such gait assistive devices to maximize one’s benefit; however, only a few studies so far have investigated methods to systematically customize the controller of assistive devices. Conventionally, researchers have used manual tuning to individualize the assistance of exoskeletons [12] or powered prostheses [3], where the wearer or an external operator subjectively tunes the control parameters based on the user’s perception or the observation of gait kinematics/kinetics. A challenge with a manual parameter tuning process is that it can involve a significant level of human subjective intervention, thus requiring expert knowledge and experience with the hardware. A more recent approach is human-in-the-loop optimization, where an optimization algorithm finds the optimal parameters that maximize one’s metabolic benefit, estimating the wearer’s instantaneous metabolic cost while walking [11, 20, 21, 22]. This approach holds advantages in that it automatically optimizes control parameters by directly monitoring the user’s metabolic cost; however, the current approach requires a user to wear respiratory measurement equipment throughout the process. The field of prosthetics has made efforts to bridge the gap between these two approaches [23, 24, 25, 26, 27]. Researchers have derived dynamic models of locomotion with specific types of powered prostheses and used computational algorithms, such as supervised learning [24], extremum seeking controller (ESC) [25], or adaptive dynamic programming (ADP) [26], to find optimal impedance control parameters in the model for each individual. Among them, Huang et al. suggested a method called cyber-expert tuning system for a powered knee prosthesis, where they implemented several decision rules of manual tuning into a computational algorithm based on data from the device’s own wearable sensors [27]. The approach of performing automatic parameter tuning with only device sensors is appealing as it opens the door to this being performed outside of a lab setting. However, it remains unclear how this approach can be applied to the devices augmenting the gait of healthy individuals, because it is currently unclear what may be proxy objective metrics for metabolic cost and how those metrics can be measured by body-worn sensors. Therefore, if a control tuning method can be developed based on an objective function that is easily measurable and strongly correlated with metabolic cost, it may greatly improve the energetic efficacy of a gait assistive device for healthy individuals.
In this paper, we present an updated version of the autonomous multi-joint soft exosuit aimed at overground walking in outdoor settings [28]. In addition, we propose an online parameter tuning method that automatically customizes assistance based on the positive power delivered to the ankle by the exosuit. This is based on the assumption that a positive correlation exists between the positive ankle augmentation power and the corresponding metabolic benefit [6, 10, 19, 29, 30, 31, 32, 33]. Given that this proxy objective metric can easily be measured by wearable sensors, we believe this augmentation-power-based parameter tuning approach holds a promise, given the desire to enable control individualization in unconstrained environments. Additionally, we present results from human subject testing demonstrating the metabolic efficacy of the soft exosuit with the subject-specific control parameters during loaded walking.
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